An Optimization Method for Joint Control of Merging Zones in Urban Tunnels of Considerable Length
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摘要: 针对城市超长隧道合流区的交通拥堵问题，研究了融合主线可变限速与匝道信号控制的联合优化控制方法。根据合流瓶颈区及其下游的不同交通状态组合，生成了四级控制策略。通过综合考虑匝道汇入、路段间速度差异和驾驶人服从度，对传统元网络（meta network，METANET）模型进行了改进；同时，通过新增匝道排队容量控制机制，对经典ALINEA算法进行了扩展，实现了可变限速与匝道信号的联合控制。在此基础上，结合模型预测控制方法，对不同交通状态下的限速值和匝道信号配时进行了优化。依托VISSIM仿真平台，构建武汉两湖隧道场景，并利用COM接口与Python二次开发，实现仿真路段交通参数的实时获取与控制。实验对比多种管控策略，包括动态可变限速、匝道信号控制与联合控制。仿真结果表明，①提出的联合管控策略相比无管控策略，减少瓶颈区车辆行程时间17.7%，降低车均延误时间62.96%；②与单一控制策略相比，联合管控可显著提高平均车速和交通流稳定性，特别是在高流量拥堵情况下效果更为显著；③在联合控制策略下，路段最低平均车速提升20.38%，瓶颈区及下游缓行时间减少22.2%，低速区域的空间范围和持续时间均明显缩短，且车速波动幅度大幅减小。面对多种复杂交通流，联合控制策略展现出良好的动态自适应能力，能够根据流量结构自动调整主线与匝道的控制强度，实现对瓶颈区交通负荷的合理分配。Abstract: To address traffic congestion at merging zones in urban tunnels of considerable length, an optimization method for joint control that integrates variable speed limits in mainlines and signal control at ramps is proposed. A four-level control strategy is developed based on the combination of different traffic states in merging bottleneck area and downstream section. The traditional meta network (METANET) model is modified by comprehensively considering ramp inflow, speed differences among sections, and driver compliance. Meanwhile, the classical ALINEA algorithm is extended by introducing a control mechanism for queue capacity at ramps, enabling the integration of variable speed limits and ramp signal control. On this basis, a model predictive control approach is employed to optimize speed limits and ramp signal timings under different traffic states. Using the VISSIM simulation platform, the scenario of Lianghu Tunnel in Wuhan is developed, which allows to acquire and control the traffic parameters in real-time through the COM interface and secondary development with Python. Various control strategies are compared, including dynamic variable speed limits, ramp signal control, and joint control. Simulation results show that: ①Compared to the situation with no control, the proposed joint control strategy reduces travel time in the bottleneck area by 17.7% and decreases the average delay time per vehicle by 62.96%. ②Compared to single control strategy, the joint control strategy significantly improves average speed and stability of traffic flow, with especially notable effects under heavy congestion conditions. ③Under the joint control strategy, the minimum average speed at road sections increases by 20.38%, the duration of slow traffic in the bottleneck area and at the downstream section decreases by 22.2%, and both the spatial scope and duration of low-speed regions are significantly reduced with a substantial decrease in speed fluctuations. When facing various complex traffic flow conditions, the joint control strategy demonstrates good dynamic adaptability, automatically adjusting the control strength of the mainline and ramp according to the flow structure, thus achieving a rational distribution of traffic load in the bottleneck area.
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Key words:
- traffic control /
- variable speed limit /
- ramp metering control /
- urban tunnel
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图 1 城市隧道合流区联合控制系统场景
Figure 1. Joint control system scenario for urban tunnel merging area
图 6 不同控制策略评价指标对比图
Figure 6. Comparison chart of evaluation metrics for different control strategies
图 7 不同信号周期下瓶颈区行程时间均值和标准差
Figure 7. Mean and standard deviation of average travel time in the bottleneck area under different signal cycles
图 8 不同信号周期下瓶颈区延误均值和标准差
Figure 8. Mean and standard deviation of delay in the bottleneck area under different signal cycles
图 9 主线高流量、匝道低流量限速值及绿信比
Figure 9. Speed limit values and green signal ratios for high mainline flow and low ramp flow
图 10 主线低流量、匝道高流量限速值及绿信比
Figure 10. Speed limit values and green signal ratios for low mainline flow and high ramp flow
图 11 主线和匝道高流量限速值及绿信比
Figure 11. Speed limit values and green signal ratios for high mainline and ramp flow
表 1 METANET模型符号及含义
Table 1. METANET model symbols and meanings
符号 含义 单位 Li 路段i的长度 km vi(k) k时刻路段i的平均速度 km/h ρi(k) k时刻路段i的平均密度 veh/km T 周期 s τ 时间滞后系数 f 期望系数 m 模型参数，防止ρi(k) 过小 vf, i 路段i的自由流速度 km/h αi 模型系数 ρcr(k) 路段临界密度 veh/km V[ρi(k)] 期望速度 km/h 
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表 2 不同控制策略评价指标均值和标准差
Table 2. Mean and standard deviation of evaluation metrics for different control strategies
评价指标 管控策略 无管控 VSL 匝道信号控制 联合管控 均值 标准差 均值 标准差 均值 标准差 均值 标准差 瓶颈区车均行程时间 18.81 1.75 15.89 1.59 18.32 2.15 15.48 1.38 瓶颈下游车均行程时间 39.08 0.22 38.42 0.16 39.10 0.21 38.43 0.15 瓶颈区平均车速 22.83 3.90 25.06 3.52 22.85 4.52 25.95 3.22 瓶颈区车均延误 27.67 18.03 11.42 5.22 23.67 17.71 10.25 4.20
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表 3 3种流量输入组合值
Table 3. Three traffic input combinations
仿真时间/s 流量输入组合 主线高、匝道低 主线低、匝道高 主线高、匝道高 主线 匝道 主线 匝道 主线 匝道 > 0~1 800 4 000 600 3 000 800 3 500 750 > 1 800~3 600 4 200 620 3 200 850 3 800 800 > 3 600~5 400 4 500 620 3 200 900 4 200 800 > 5 400~7 200 4 000 600 3 000 800 4 000 750
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